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In this work we present a method for automated classification of endoscopic images according to the pit pattern classification scheme. Images taken during colonoscopy are transformed to the wavelet domain using the pyramidal discrete wavelet transform. Then, Gaussian Markov random fields are used to extract features from the resulting wavelet coefficients.(More)
In this work we present a method for automated classification of endoscopic images according to the pit pattern classification scheme. Images taken during colonoscopy are transformed using a modified version of the local binary patterns operator (LBP). Then, two-dimensional histograms based on the LBP data from different color channels are created. Finally,(More)
Histogram-based techniques for an automated classification of magnifying endoscope images with respect to pit patterns of colon lesions are discussed and compared. Currently, the results only allow a support of human observation especially due to the large number of false negatives of neoplastic lesions
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In this paper, we present a novel approach to predict the histological diagnosis of colorectal lesions from high-magnification colonoscopy images by means of Pit Pattern analysis. Motivated by the shortcomings of discriminant classifier approaches, we present a generative model based strategy which is closely related to content-based image retrieval (CBIR)(More)
In this work we present a method for an automated classification of en-doscopic images according to the pit pattern classification scheme. Images taken during colonoscopy are transformed using an extended and rotation invariant version of the Local Binary Patterns operator (LBP). The result of the transforms is then used to extract polygons from the images.(More)
This work describes an experimental study on the classification of images taken from colonoscopy. An emphasis is devoted to the procedure of finding features which allow an adequate classification. The proposed approach applies filters to the images' respective Fourier domains. Good configurations of these filters are obtained using a genetic algorithm,(More)
This paper describes an application of machine learning techniques and evolutionary algorithms to colon cancer diagnosis. We propose an automated classification system for endoscopical images, which is supposed to support physicians in making correct decisions. Classification is done according to the pit-pattern scheme, which defines two/six different(More)
OBJECTIVE There is evidence of an interaction between psychological factors and activity of inflammatory bowel disease (IBD). We examined the influence of depressive mood and associated anxiety on the course of IBD over a period of 18 months in a cohort of patients after an episode of active disease. METHODS In this prospective, longitudinal,(More)
The effects of the benzodiazepine antagonist flumazenil were studied in 20 episodes of hepatic encephalopathy (HE) in 17 patients with acute (n = 9) or chronic (n = 8) liver failure who had not responded to conventional therapy. Patients with a history of benzodiazepine intake were excluded. Changes in HE stage, in Glasgow coma scale, and in somatosensory(More)